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## Description
- Fixed a bug in `libs/sdk-py/langgraph_sdk/auth/__init__.py` where the
error message for an already-set authentication handler did not properly
render the handler value.
- Updated the error string from a static `{self._authenticate_handler}`
to a correctly interpolated f-string.
```python
"Authentication handler already set as {self._authenticate_handler}."
```
```python
f"Authentication handler already set as {self._authenticate_handler}."
```
- Error messages now correctly display the actual handler instance,
improving debugging clarity.
- **Issue:** Fixes https://github.com/langchain-ai/langgraph/issues/6387
- **Dependencies:** -
- **Twitter handle:** -
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
1. A test for the integration, preferably unit tests that do not rely on
network access,
2. An example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. We will not consider a
PR unless these three are passing in CI. See [contribution
guidelines](https://github.com/langchain-ai/langgraph/blob/main/CONTRIBUTING.md)
for more.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to `pyproject.toml` files (even
optional ones) unless they are **required** for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
LangGraph Python SDK
This repository contains the Python SDK for interacting with the LangSmith Deployment REST API.
Quick Start
To get started with the Python SDK, install the package
pip install -U langgraph-sdk
You will need a running LangGraph API server. If you're running a server locally using langgraph-cli, SDK will automatically point at http://localhost:8123, otherwise
you would need to specify the server URL when creating a client.
from langgraph_sdk import get_client
# If you're using a remote server, initialize the client with `get_client(url=REMOTE_URL)`
client = get_client()
# List all assistants
assistants = await client.assistants.search()
# We auto-create an assistant for each graph you register in config.
agent = assistants[0]
# Start a new thread
thread = await client.threads.create()
# Start a streaming run
input = {"messages": [{"role": "human", "content": "what's the weather in la"}]}
async for chunk in client.runs.stream(thread['thread_id'], agent['assistant_id'], input=input):
print(chunk)